Action-Conditioned Frame Prediction Without Discriminator

Abstract

Predicting high-quality images that depend on past images and external events is a challenge in computer vision. Prior proposals have tried to solve this problem; however, their architectures are complex, unstable, or difficult to train. This paper presents an action-conditioned network based upon Introspective Variational Autoencoder (IntroVAE) with a simplistic design to predict high-quality samples. The proposed architecture combines features of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) with encoding and decoding layers that can self-evaluate the quality of predicted frames; no extra discriminator network is needed in our framework. Experimental results with two data sets show that the proposed architecture could be applied to small and large images. Our predicted samples are comparable to the state-of-the-art GAN-based networks.